Wireless communication systems are widely known in which a base station (BS) communicates with multiple subscriber stations (SSs) or mobile stations (MSs) within range of the BS. The terms subscriber station (SS) and mobile station (MS) may be considered interchangeable for the purposes of this specification, and both SSs and MSs may be referred to generically as users or user stations. Also, whilst the terms subscriber station (SS) and mobile station (MS) may be used interchangeably, the term “mobile” in particular should not be construed to necessarily imply that the user station (etc) must always be movable. In many cases it will be movable (e.g. a mobile handset). However, wireless communication systems (and the present invention) can also operate where the SS/user/user station is fixed in position at a particular location.
The area covered by one BS is called a cell and typically, many base stations (BSs) are provided in appropriate locations so as to cover a wide geographical area more or less seamlessly with adjacent cells. Each BS divides its available bandwidth, i.e. frequency and time resources, into individual resource allocations for the users. There is a constant need to increase the capacity of such systems in order to accommodate more users and/or more data-intensive services.
OFDM (Orthogonal Frequency Division Multiplexing) is one known technique for transmitting data in a wireless communication system. An OFDM-based communications scheme divides data symbols to be transmitted among a large number of subcarriers (also called frequency fingers), hence the term frequency division multiplexing. By carrying only a small amount of data on each subcarrier, the bit rate per subcarrier is kept low and hence inter-symbol interference is reduced. Data is modulated onto a subcarrier by adjusting its phase, amplitude, or both phase and amplitude.
The “orthogonal” part of the name OFDM refers to the fact that the spacings of the subcarriers are specially chosen so as to be orthogonal, in a mathematical sense, to the other subcarriers. This means that they are arranged along the frequency axis such that the sidebands of adjacent subcarriers are allowed to overlap but can still be received without inter-subcarrier interference. In mathematical terms, the sinusoidal waveforms of each subcarrier are called eigenfunctions of a linear channel, with the peak of each sinusoid coinciding with a null of every other sinusoid. This can be achieved by making the subcarrier spacing a multiple of the reciprocal of the symbol period.
When individual subcarriers or sets of subcarriers are assigned to different users of the wireless communication system, the result is a multi-access system referred to as OFDMA (Orthogonal Frequency Division Multiple Access). The term OFDM as used in the art is often intended to include OFDMA. The two terms may therefore be considered interchangeable for the purposes of the present explanation. By assigning distinct frequency/time resources to each user in a cell, OFDMA can substantially avoid interference among the users within a cell.
A further modification of the basic OFDM scheme is called MIMO-OFDM, where MIMO stands for multiple-input multiple-output. This type of scheme employs multiple antennae at the transmitter and/or at the user receiver (often at both) to enhance the data capacity achievable between the BS and each user station. For example, a 2×2 MIMO configuration contains two antennae at the transmitter and two antennae at the receiver; a 4×4 MIMO configuration contains four antennae at the transmitter and four antennae at the receiver. There is no need for the transmitter and receiver to employ the same number of antennae. Typically, a base station in a wireless communication system will be equipped with many more antennae in comparison with a mobile station (such as, for example, a mobile handset), owing to differences in power, cost and size limitations.
The term MIMO channel is used to describe the frequency (or equivalently time delay) response of the radio link between a transmitter and a receiver. The so-called MIMO channel (or “channel”) contains all the sub-carriers, and covers the whole bandwidth of transmission. A MIMO channel contains many individual radio links. The number of these individual radio links, which may be individually referred to as single-input single-output (SISO) channels (also called sub-channels), is Nt×Nr, where Nt is the number of antennae at the transmitter and Nr is the number of antennae at the receiver. For example, a 3×2 MIMO arrangement contains 6 links, hence it has 6 SISO channels.
Considering the simplified 3×2 MIMO system schematically represented in FIG. 1, it can be seen that antenna R0 of receiver R receives transmissions from each of the transmitter antennae T0, T1 and T2 of transmitter T. Similarly, receiver antenna R1 receives transmissions from transmitter antennae T0, T1 and T2. Therefore, the signal received at the receiver comprises (or is made up of) some combination of the transmissions (i.e. of the SISO channels) from the transmitter antennae. In general, SISO channels can be combined in various ways to transmit one or more data streams to the receiver.
FIG. 2 is a conceptual diagram of a more generalized MIMO system. In FIG. 2, a transmitter transmits signals utilizing Nt transmitting antennae, and a receiver receives the signals from the transmitter utilizing Nr receiving antennae. In order to create a mathematical model of the characteristics of the overall MIMO channel, it is necessary to represent the individual SISO channels between the transmitter and receiver. As shown in FIG. 2, the individual SISO channels are represented by H0,0 to HNr-1, Nt-1, and as suggested in the Figure, these form terms of a matrix commonly called the channel matrix or channel response matrix H. “H0,0” represents the channel characteristics (for example, channel frequency response) for transmitting signals from the transmitting antenna 0 to the receiving antenna 0. Similarly, “HNr-1, Nt-1” represents the channel characteristics for transmitting signals from the transmitting antenna Nt−1 to the receiving antenna Nr−1, and so on.
In FIG. 2, the symbols x0 to xNt-1, which represent the signal elements transmitted using the transmitting antennae 0 to Nt−1, together form a transmitted signal vector x (i.e. x=(x0, x1, x2, . . . , xNT-1)T). Likewise, the received signals elements y0 to yNr-1 received by receiving antennae 0 to Nr−1 together form a received signal vector y (i.e. y=(y0, y1, y2, . . . , yNr-1)T). The relationship between the vectors y and x may be modelled by the following basic mathematical MIMO system equation:y=Hx+n  (I)where H is the channel matrix described above and n is a vector representing noise. Noise elements n0 to nNr-1 are illustrated in FIG. 2 and represent noise in the respective received signal elements y0 to yNr-1. Hence, the noise vector n is given by n=(n0, n1, n2, . . . , nNr-1)T. It is generally assumed for the purposes of the model given by equation (I) that the noise represented by vector n is Gaussian white noise with zero mean and variance σ2.
The channel matrix H has a rank which is the number of linearly independent rows or columns thereof. When some of the rows or columns are linearly dependent, this indicates (and represents) correlation between individual subchannels (i.e. correlation between individual SISO channels) in the MIMO channel, and the channel matrix is said to be “rank deficient”. When there is correlation between sub-channels, conventional receivers tend to perform poorly and the MIMO channel is incapable of providing the maximum data throughput.
It should be noted that, despite the name “multiple-input multiple-output”, MIMO systems can operate even if one of the transmitter and the receiver has only one antenna (i.e. even if Nt=1 or Nr=1). In fact, MIMO systems might technically be said to operate even where the transmitter and the receiver both have only one antenna (i.e. where Nt=Nr=1), although this situation might be considered to constitute a special case because, in the mathematical model of the equation (I), the MIMO channel would then be represented by a scalar h rather than matrix H.
MIMO transmission schemes may be described as “non-adaptive” and “adaptive”. In the non-adaptive case, the transmitter does not have any knowledge of the channel properties and this limits performance, as it cannot take account of changes in conditions (channel profile). Adaptive schemes rely on the feedback of information (channel-state information or CSI) from the receiver to the transmitter, allowing modification of the transmitted signal to account for changing conditions and to maximise data throughput. The present invention is concerned, at least primarily, with these adaptive MIMO schemes.
The feedback just described is important, in particular, in FDD (Frequency Division Duplex) systems, where uplink transmissions (i.e. transmissions from user station to base station) and downlink transmissions (vice-versa) employ two different carrier frequencies. Because of the frequency change, the uplink and downlink channels are different and CSI needs to be fed back in order to provide an adaptive scheme.
Aspects of the present invention could potentially find application in both downlink (i.e. transmissions from base station(s) acting as transmitter(s) to user(s) acting as receiver(s)) and uplink (i.e. transmissions from user(s) acting as transmitter(s) to base station(s) acting as a receiver(s)). However, at least in relation to certain embodiments, it is envisaged that the invention may be used to realise particular improvements in downlink transmissions. Therefore, whilst no limitation should be implied as to whether the invention may be applied to uplink or downlink transmissions, the invention will be described primarily with respect to downlink transmissions.
FIG. 3 is a diagram representing a MIMO system similar to that shown in FIG. 1, but more generalised. MIMO system 1 comprises a transmitter 2 which comprises a plurality of transmitting antennae (0), (1), . . . , (N1−1) and a receiver 3 which comprises a plurality of receiving antennae (0), (1), . . . , (Nr−1). The transmitter 2 transmits symbols 0, 1, . . . , Nt−1 using the Nt transmitting antennae. The symbols can be created from one data stream, referred to as vertical encoding, or different data streams, referred to as horizontal encoding. In addition, each transmitted symbol corresponds to, for example, one-bit data if the modulation method is binary phase-shift keying (BPSK), or corresponds to two-bit data if the modulation method is quadrature phase-shift keying (QPSK). These concepts will be familiar to those skilled in the art. The receiver 3 receives the signals transmitted from the transmitting device 2 using the Nr receiving antennae, and it comprises a signal regeneration unit 4 which regenerates the transmitted symbols from the signals received.
As indicated by the arrows in FIG. 3, the signals transmitted from a plurality of the transmitting antennae are received by a plurality of receiving antennae, giving rise to Nt×Nr possible subchannels in total. In other words, the signals transmitted from the transmitting antenna (0) are received by receiving antennae (0) through (Nr−1), the signals transmitted from the transmitting antenna (1) are received by receiving antennae (0) through (Nr−1), etc. The characteristics of the subchannel which propagates the signals from the i-th transmitting antenna to the j-th receiving antenna are expressed as “Hji” and form one component term of the Nr×Nt channel matrix H. Those skilled in the art will recognise that if no signal is transmitted from a particular transmitting antenna i to a particular receiving antenna j, then the component Hji representing that sub-channel in the channel matrix H would be zero.
By way of further background explanation, a MIMO-OFDM transmitter and a MIMO-OFDM receiver will be briefly outlined with reference to FIGS. 4 and 5 respectively. In the OFDM transmitter schematically shown in FIG. 4, high-speed binary data is encoded (convolutional code is an example), interleaved, and modulated (using a modulation scheme such as BPSK, QPSK, 64QAM, and the like). Independent channel encoders may be used for each transmitting antenna. Subsequently, the data is converted into parallel low-speed modulated data streams which are fed to N sub-carriers. The output from each encoder is carried separately on a plurality of sub-carriers. The modulated signals are frequency-division multiplexed by N-point Inverse Fast Fourier Transform (IFFT) and the guard interval is added. The resulting OFDM signal is converted into an analog signal by a D/A converter and is upconverted into RF band and transmitted over the air.
At the MIMO-OFDM receiver schematically shown in FIG. 5, the received signals from the Nr receiver antennae are filtered by a band pass filter (BPF), and then down-converted to a lower frequency. The down-converted signal is sampled by A/D converter (namely, converted into a digital signal), and the guard interval is removed before the sampled data is fed to the N-point Fast Fourier Transformer (FFT). After Fourier transformation is performed on each of the signals received through the Nr receiver antennae, they are fed to the MIMO signal processing unit 11. The MIMO signal processing unit 11 comprises the signal regeneration unit 4 (as shown in FIG. 3) which performs processing (discussed further below) to compensate for the channel characteristics.
The discussion above of the transmitter (FIG. 4) and receiver (FIG. 5) is given by way of summary explanation only. Those skilled in the art will be generally familiar with such devices and the principles involved in their operation. It should also be noted that the above explanation has considered the case of a single transmitter sending MIMO signals to a single receiver, but of course a practical MIMO wireless communication system may be much more elaborate than this, providing many mutually-adjacent cells in each of which a base station transmits over respective MIMO channels to multiple user stations simultaneously.
Referring again to the basic MIMO system model represented by the equation (I) above, it will be recalled that when some of the rows or columns of the channel matrix H are linearly dependent (i.e. when the channel matrix H is rank deficient), this indicates that there is correlation between sub-channels. It will also be recalled that, where such correlation exists, conventional receivers tend to perform poorly.
A technique known as Lattice Reduction (LR) has been proposed for providing significant performance improvements in correlated MIMO channels. MIMO systems which utilise lattice reduction may be referred to as LR-MIMO systems. In mathematics generally, the goal of lattice reduction is, given a set of basis vectors for a lattice, to find a basis with short, nearly orthogonal vectors. For example, FIG. 6 gives a diagrammatic example of lattice reduction in two dimensions. In FIG. 6, the vectors a1 and a2 are the given basis vectors for the lattice represented by the vertices (i.e. the dots). The vectors b1 and b2 are nearly orthogonal basis vectors obtained using lattice reduction.
In the context of MIMO wireless communication systems having correlated MIMO channels, the purpose of lattice reduction is to transform the channel matrix H into a form where the rows and columns are more nearly orthogonal (i.e. so that the rows and columns are, in effect, more nearly linearly independent). Doing this helps to minimise the detrimental effects of correlation mentioned above. The matrix by which this transformation of the channel matrix H is affected is generally called the Lattice Reduction Matrix P.
In many practical implementations of MIMO wireless communication systems, the user station may be, for example, a mobile handset, and may therefore have limited resources in terms of power, data processing/computational capacity and data transmission/reception capacity. Certainly, the power, processing and transmission/reception resources in mobile handsets and the like are generally much more limited than the power, processing and transmission/reception resources of a base station (BS). This is a significant impediment to, and significantly increases costs associated with, practical implementations of LR-MIMO systems.
The impediment created by the limited resources of a user station compared with a base station (BS) is particularly significant in relation to the burden placed on the data processing and transmission/reception resources of the user station, and especially where the user station is a mobile station (e.g. a mobile handset), although it may often also be significant where the user station is of a fixed position type. Aspects of the present invention may go at least some way to helping address this problem.
Also, the limited resources of a user station make it difficult, or at least less preferable, to calculate the lattice reduction matrix P at the receiver. It would therefore be desirable to calculate the lattice reduction matrix at the transmitter and to transmit the Lattice Reduction Matrix P from the transmitter to the receiver. However, this would be in addition to the signal x which needs to be transmitted from the transmitter to the receiver. Therefore, the need to transmit the Lattice Reduction Matrix P imposes an additional burden on the transmission resources of the system. Aspects of the present invention may at least help to reduce this additional transmission burden as well.